Semantic Relations Established by Specialized Processes Expressed by Nouns and Verbs: Identification in a Corpus by means of Syntactico-semantic Annotation
Bibliographic record
Abstract
This article presents the methodology and results of the analysis of terms referring to processes expressed by verbs or nouns in a corpus of specialized texts dealing with ceramics.Both noun and verb terms are explored in context in order to identify and represent the semantic roles held by their participants (arguments and circumstants), and therefore explore some of the relations established by these terms.We present a methodology for the identification of related terms that take part in the development of specialized processes and the annotation of the semantic roles expressed in these contexts.The analysis has allowed us to identify participants in the process, some of which were already present in our previous work, but also some new ones.This method is useful in the distinction of different meanings of the same verb.Contexts in which processes are expressed by verbs have proved to be very informative, even if they are less frequent in the corpus.This work is viewed as a first step in the implementation -in ontologies -of conceptual relations in which activities are involved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".